{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Week1基础作业\n",
    "\n",
    "姓名：张君仪"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np  # 矩阵操作\n",
    "import pandas as pd # SQL数据处理\n",
    "\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "\n",
    "import matplotlib.pyplot as plt   #画图\n",
    "import seaborn as sns\n",
    "import random\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#读取数据\n",
    "data=pd.read_csv(\"day.csv\")\n",
    "#分离输入特征x和输出y\n",
    "y=data['cnt'].values\n",
    "X=data.drop(['casual','registered','cnt'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 适当的特征工程(及数据探索)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f90b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#目标y（总租车人数）的直方图/分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['cnt'].values,kde=True)\n",
    "plt.xlabel('Number of Total Rentals a Day', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114012d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data['cnt'].values,color='pink')\n",
    "plt.title(\"Distribution of Total Rentals\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大多数租车总数集中在4000-6000之间，在均值4504附近，与正态分布很接近，但租车数在6000-8000的样本数也很多。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "season,yr,mnth,holiday,weekday,workingday,weathersit都为类别特征，就不继续画直方图了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114055898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values, bins=30, kde=False)\n",
    "plt.xlabel('Temperature', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142ea518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data['atemp'].values, bins=30, kde=False)\n",
    "plt.xlabel('\"Feels like\" Temperature', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度直方图的分布与实际温度直方图分布类似，可能两个特征可以二选一。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143de978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data['hum'].values, bins=30, kde=False)\n",
    "plt.xlabel('Humidity', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114617940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data['windspeed'].values, bins=30, kde=False)\n",
    "plt.xlabel('Wind Speed', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#转换dteday\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "values = array(data['dteday'])\n",
    "label_encoder = LabelEncoder()\n",
    "integer_encoded = label_encoder.fit_transform(values)\n",
    "data=data.drop('dteday',axis=1)\n",
    "data['dteday']=integer_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns\n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "#去掉instant列\n",
    "data_corr = data.drop('instant',axis=1).corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114623e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('rental_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到temp和atemp关联性接近1，与cnt的关联度都为0.15，结合之前temp和atemp的分布图，所以我们可以只取其中一个，比如只取temp。\n",
    "\n",
    "另外，yr与dteday也可drop\n",
    "\n",
    "所以丢弃instant，atemp,yr和dteday。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#分离输入特征x和输出y\n",
    "y=data['cnt'].values\n",
    "X=data.drop(['casual','registered','cnt'],axis=1)\n",
    "#分割数据\n",
    "X_train=X[X['yr']==0]\n",
    "X_test=X[X['yr']==1]\n",
    "y_train=data[data['yr']==0]['cnt'].values\n",
    "y_test=data[data['yr']==1]['cnt'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_class=pd.get_dummies(X_train,columns=['season','mnth','holiday','weekday','workingday','weathersit']).values\n",
    "X_test_class=pd.get_dummies(X_test,columns=['season','mnth','holiday','weekday','workingday','weathersit']).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(365, 37)\n",
      "(366, 37)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_class.shape)\n",
    "print(X_test_class.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train_num=X_train[['temp','hum','windspeed']].values\n",
    "X_test_num=X_test[['temp','hum','windspeed']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(365, 3)\n",
      "(366, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_num.shape)\n",
    "print(X_test_num.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 数据归一化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的归一化器\n",
    "mms_X = MinMaxScaler()\n",
    "mms_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行归一化处理\n",
    "X_train_num = mms_X.fit_transform(X_train_num)\n",
    "X_test_num = mms_X.transform(X_test_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#合并成X_train, X_test\n",
    "X_train =np.concatenate((X_train_class,X_train_num),axis=1)\n",
    "X_test =np.concatenate((X_test_class,X_test_num),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(365, 40)\n",
      "(366, 40)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train=y_train.reshape(-1,1)\n",
    "y_test=y_test.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 确定模型类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 尝试缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用归一化后的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is -3.46321088956\n",
      "The r2 score of LinearRegression on train is 0.841577379733\n"
     ]
    }
   ],
   "source": [
    "print ('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11581c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"LR Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "残差分布基本和高斯分布匹配，略微右skew。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 正则化的线性回归（L2正则 --> 岭回归）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 使用归一化数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is -3.43701930512\n",
      "The r2 score of RidgeCV on train is 0.841577212209\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = np.linspace(0.001,500,100)#形成100个在0.001到500范围内的递增小数\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MvDkzmzOzecSIEXUosiRJ/UOnIZ2ZV2XmmMwcC3wY+FlmngvMAT5YbTYTeKDHSilJUj+0M9dJXwF8MiKWUfuO+tv1KZIkSQIY2Pkmf5aZc4G51fRzwLH1L5IkSQLvOCZJUrEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVADG10ASf1DS0vPbCvtymxJS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUAMbXQBJO6+lpWe2ldRYtqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhOg3piBgcEU9ExC8j4umI+Ntq+biImB8RyyLinoh4W88XV5Kk/qMrLek3gfdm5kRgEnBaRBwPfAn4SmYeAvwBmNVzxZQkqf/pNKSzZn0121S9EngvcH+1/DZgRo+UUJKkfqpLT8GKiAHAQuAQ4CbgN8DLmbmx2mQFsH87770YuBjgwAMP3NnyStpJPgVL6ju6NHAsMzdl5iRgDHAscHhXD5CZN2dmc2Y2jxgxopvFlCSp/9mh0d2Z+TIwB5gCDI2ILS3xMcDKOpdNkqR+rSuju0dExNBqenfgZGAJtbD+YLXZTOCBniqkJEn9UVe+kx4N3FZ9L70bcG9m/jgingHujojPA78Avt2D5ZQkqd/pNKQz80lgchvLn6P2/bQkSeoB3nFMkqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkq1MBGF0CSttfSUt/tpL7KlrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgo1sNEFkNS+lpZGl0BSI9mSliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJheo0pCPigIiYExHPRMTTEXFZtXxYRDwSEUurf/fp+eJKktR/dKUlvRG4PDPHA8cDH4+I8cCVwOzMfBcwu5qXJEl10mlIZ+aqzFxUTa8DlgD7A2cBt1Wb3QbM6KlCSpLUH+3QU7AiYiwwGZgPjMrMVdWq54FR7bznYuBigAMPPLC75ZT6hK4+tcqnW0nqii4PHIuIIcAPgL/JzFdbr8vMBLKt92XmzZnZnJnNI0aM2KnCSpLUn3QppCOiiVpA35mZP6wWr46I0dX60cALPVNESZL6p66M7g7g28CSzPxyq1UPAjOr6ZnAA/UvniRJ/VdXvpM+Afgo8K8RsbhadjXwReDeiJgFLAfO7pkiSpLUP3Ua0pn5z0C0s3pafYsjSZK28I5jkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpELt0FOwJKkkPnVMuzpb0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCuUlWFIDeEmQpK6wJS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVyqdgSVJlR55O5pPM1BtsSUuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmF8ilYknZ5PrFKfZUtaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhfISLKkTXr4jqVFsSUuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEJ1GtIR8Z2IeCEinmq1bFhEPBIRS6t/9+nZYkqS1P90pSV9K3DadsuuBGZn5ruA2dW8JEmqo05DOjPnAS9tt/gs4LZq+jZgRp3LJUlSv9fd76RHZeaqavp5YFSdyiNJkio7PXAsMxPI9tZHxMURsSAiFqxZs2ZnDydJUr/R3ZBeHRGjAap/X2hvw8y8OTObM7N5xIgR3TycJEn9T3dD+kFgZjU9E3igPsWRJElbdOUSrLuAfwEOi4gVETEL+CJwckQsBd5XzUuSpDoa2NkGmfkX7ayaVueySJKkVrzjmCRJhTKkJUkqVKfd3ZKkt2ppqe92UltsSUuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmF8ilY6rd8OpGk0tmSliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgrlU7DUZ3T1qVU+3UrSrsKWtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQnkJlhrKy6UkqX22pCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFSoys9cO1tzcnAsWLOi146lxvLRK2jE98X/GJ8eVKSIWZmZzV7a1JS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVA+BauH7cilDX3hMoi+UEapL9rVfleoPmxJS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqVJ++BKsnLlnoC0+N2ZXORdKOq/f/2f76+6wv/I60JS1JUqEMaUmSCmVIS5JUqJ0K6Yg4LSKejYhlEXFlvQolSZJ2IqQjYgBwE/B+YDzwFxExvl4FkySpv9uZlvSxwLLMfC4z/wTcDZxVn2JJkqTIzO69MeKDwGmZeWE1/1HguMz8q+22uxi4uJo9DHi2+8Utyr7Ai40uRD9kvTeG9d4Y1ntj9HS9H5SZI7qyYY9fJ52ZNwM39/RxeltELMjM5kaXo7+x3hvDem8M670xSqr3nenuXgkc0Gp+TLVMkiTVwc6E9P8D3hUR4yLibcCHgQfrUyxJktTt7u7M3BgRfwX8FBgAfCczn65bycq3y3Xh9xHWe2NY741hvTdGMfXe7YFjkiSpZ3nHMUmSCmVIS5JUKEO6DRFxfUT8KiKejIgfRcTQVuuuqm6D+mxEnNpqeZu3SK0G1s2vlt9TDbJTGyLiQxHxdERsjojm7dZZ7w3grX/rKyK+ExEvRMRTrZYNi4hHImJp9e8+1fKIiBurun8yIo5u9Z6Z1fZLI2JmI86lL4mIAyJiTkQ8U/2OuaxaXn7dZ6av7V7AKcDAavpLwJeq6fHAL4FBwDjgN9QGzQ2opg8G3lZtM756z73Ah6vpbwGXNPr8Sn0BR1C74c1coLnVcuu9MZ9Hu/Xrq9t1ehJwNPBUq2X/AFxZTV/Z6vfNB4CHgACOB+ZXy4cBz1X/7lNN79Pocyv5BYwGjq6m9wR+Xf1eKb7ubUm3ITMfzsyN1ezPqV0DDrXbnt6dmW9m5m+BZdRuj9rmLVIjIoD3AvdX778NmNFb59HXZOaSzGzrjnTWe2N46986y8x5wEvbLT6L2s8obPuzehZwe9b8HBgaEaOBU4FHMvOlzPwD8AhwWs+Xvu/KzFWZuaiaXgcsAfanD9S9Id25C6j9RQW1D/X3rdatqJa1t3w48HKrwN+yXDvGem+M9upX9TUqM1dV088Do6rpHf25VxdExFhgMjCfPlD3PX5b0FJFxKPAO9pYdU1mPlBtcw2wEbizN8u2K+tKvUv9VWZmRHhdbA+JiCHAD4C/ycxXa51uNaXWfb8N6cx8X0frI+J84HRgWlZfRtDxrVDbWr6WWjfJwKpV1+9vndpZvbfDem8Mb/3bO1ZHxOjMXFV1qb5QLW+v/lcCU7dbPrcXytmnRUQTtYC+MzN/WC0uvu7t7m5DRJwGfBo4MzNfa7XqQeDDETEoIsYB7wKeoJ1bpFbhPgf4YPX+mYCtxR1nvTeGt/7tHQ9S+xmFbX9WHwTOq0YaHw+8UnXN/hQ4JSL2qUYjn1ItUzuqcSrfBpZk5pdbrSq/7hs96q7EF7WBSb8HFlevb7Vadw21Ea/PAu9vtfwD1EYM/oZa1+2W5QdTC5RlwH3AoEafX6kv4D9T+47nTWA18FPrveGfSZv166vb9XkXsArYUP2sz6I2hmI2sBR4FBhWbRvATVXd/yvbXvFwQfWzvQz4y0afV+kv4D8CCTzZ6vf6B/pC3XtbUEmSCmV3tyRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQV6v8DyDBnDtV/dzAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c0afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_ridge,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"L2 Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "岭回归中的残差分布也是略微右skew，基本和高斯分布匹配，并且test的r2 score也增加了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114845358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.001\n"
     ]
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到最佳参数大概为0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[431] [6043]\n",
      "[-726.97087793] [ 5355.79460774]\n"
     ]
    }
   ],
   "source": [
    "print(min(y_train),max(y_train))\n",
    "print(min(y_train_pred_ridge),max(y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114384940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_ridge)\n",
    "plt.plot([-800,7000],[-800,7000],'--k')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True rentals')\n",
    "plt.ylabel('Predicted rentals')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测效果总体还是可以的，没有太离散的点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 正则化的线性回归（L1正则 --> Lasso）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用归一化数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is -0.0657037889477\n",
      "The r2 score of LassoCV on train is 0.831266696027\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV(alphas=alphas)\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用L1正则进行回归训练时，test的r2 score减小很多，train的r2 score还是在0.83左右。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115873208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 5.05149494949\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log(lasso.alphas_,), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最佳参数大概为5.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f978d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lasso)\n",
    "plt.plot([-600,7000],[-600,7000],'--k')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True rentals')\n",
    "plt.ylabel('Predicted rentals')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和岭回归中预测值与真值的散点图类似，没有太离散的点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2.499632e+03</td>\n",
       "      <td>[1898.48020437]</td>\n",
       "      <td>[1878.89413367]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[1499.86884582]</td>\n",
       "      <td>[1484.39513313]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.350682e+02</td>\n",
       "      <td>[701.074616199]</td>\n",
       "      <td>[697.087491807]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.727081e+02</td>\n",
       "      <td>[687.174445649]</td>\n",
       "      <td>[688.757848794]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.519341e+02</td>\n",
       "      <td>[689.702511139]</td>\n",
       "      <td>[688.093370831]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3.619165e+02</td>\n",
       "      <td>[644.119577453]</td>\n",
       "      <td>[644.002731815]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.797016e+01</td>\n",
       "      <td>[535.145441268]</td>\n",
       "      <td>[535.454296762]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.987895e+02</td>\n",
       "      <td>[506.50806517]</td>\n",
       "      <td>[507.680742499]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-8.714916e+01</td>\n",
       "      <td>[509.359298484]</td>\n",
       "      <td>[503.332776073]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[429.470743844]</td>\n",
       "      <td>[424.320413165]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[343.769521647]</td>\n",
       "      <td>[343.699147588]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.845368e+01</td>\n",
       "      <td>[252.337530996]</td>\n",
       "      <td>[252.212614822]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.937785e+02</td>\n",
       "      <td>[146.541381122]</td>\n",
       "      <td>[146.330323502]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[124.143481479]</td>\n",
       "      <td>[123.529999238]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1.494971e+01</td>\n",
       "      <td>[117.753808132]</td>\n",
       "      <td>[117.670072996]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[53.2764839081]</td>\n",
       "      <td>[53.23432534]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.905459e+01</td>\n",
       "      <td>[16.6198160286]</td>\n",
       "      <td>[16.629910627]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.925784e+00</td>\n",
       "      <td>[9.51509045691]</td>\n",
       "      <td>[9.52799561357]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[2.34835765694e-12]</td>\n",
       "      <td>[0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.412873e+00</td>\n",
       "      <td>[-2.1524506977]</td>\n",
       "      <td>[-2.13666747604]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.064718e-01</td>\n",
       "      <td>[-2.15245069759]</td>\n",
       "      <td>[-2.13666747604]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-6.043428e+01</td>\n",
       "      <td>[-24.4889109184]</td>\n",
       "      <td>[-24.5740748365]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-28.0349838856]</td>\n",
       "      <td>[-28.0558289421]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-35.3490118655]</td>\n",
       "      <td>[-35.2276105202]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-2.006340e+00</td>\n",
       "      <td>[-53.2764839081]</td>\n",
       "      <td>[-53.23432534]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-56.0158079484]</td>\n",
       "      <td>[-55.9704649384]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-131.785898247]</td>\n",
       "      <td>[-131.619497127]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-1.566316e-12</td>\n",
       "      <td>[-146.541381122]</td>\n",
       "      <td>[-146.330323502]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[-171.209462653]</td>\n",
       "      <td>[-169.044414673]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-7.459446e+01</td>\n",
       "      <td>[-175.113111934]</td>\n",
       "      <td>[-175.169337023]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-385.877226489]</td>\n",
       "      <td>[-385.406829153]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-9.769098e+02</td>\n",
       "      <td>[-612.36986033]</td>\n",
       "      <td>[-612.497574562]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-613.107647016]</td>\n",
       "      <td>[-613.2703877]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>-7.017275e+02</td>\n",
       "      <td>[-630.44488125]</td>\n",
       "      <td>[-630.612223856]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[-813.835661992]</td>\n",
       "      <td>[-770.79129466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-6.797180e+02</td>\n",
       "      <td>[-795.506199788]</td>\n",
       "      <td>[-794.536450936]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-5.073876e+00</td>\n",
       "      <td>[-821.563344135]</td>\n",
       "      <td>[-818.322389082]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-1.303545e+03</td>\n",
       "      <td>[-987.8890991]</td>\n",
       "      <td>[-987.701879402]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-1108.91666676]</td>\n",
       "      <td>[-1104.9090058]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-8.037370e+01</td>\n",
       "      <td>[-1413.95779017]</td>\n",
       "      <td>[-1408.90733573]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      coef_lasso              coef_lr        coef_ridge\n",
       "37  2.499632e+03      [1898.48020437]   [1878.89413367]\n",
       "2   0.000000e+00      [1499.86884582]   [1484.39513313]\n",
       "20  3.350682e+02      [701.074616199]   [697.087491807]\n",
       "15  6.727081e+02      [687.174445649]   [688.757848794]\n",
       "19  3.519341e+02      [689.702511139]   [688.093370831]\n",
       "34  3.619165e+02      [644.119577453]   [644.002731815]\n",
       "10  9.797016e+01      [535.145441268]   [535.454296762]\n",
       "16  4.987895e+02       [506.50806517]   [507.680742499]\n",
       "22 -8.714916e+01      [509.359298484]   [503.332776073]\n",
       "21 -0.000000e+00      [429.470743844]   [424.320413165]\n",
       "35 -0.000000e+00      [343.769521647]   [343.699147588]\n",
       "9   1.845368e+01      [252.337530996]   [252.212614822]\n",
       "23  1.937785e+02      [146.541381122]   [146.330323502]\n",
       "18  0.000000e+00      [124.143481479]   [123.529999238]\n",
       "31  1.494971e+01      [117.753808132]   [117.670072996]\n",
       "33  0.000000e+00      [53.2764839081]     [53.23432534]\n",
       "30  2.905459e+01      [16.6198160286]    [16.629910627]\n",
       "27  9.925784e+00      [9.51509045691]   [9.52799561357]\n",
       "1   0.000000e+00  [2.34835765694e-12]             [0.0]\n",
       "0   1.412873e+00      [-2.1524506977]  [-2.13666747604]\n",
       "6   1.064718e-01     [-2.15245069759]  [-2.13666747604]\n",
       "25 -6.043428e+01     [-24.4889109184]  [-24.5740748365]\n",
       "26 -0.000000e+00     [-28.0349838856]  [-28.0558289421]\n",
       "28 -0.000000e+00     [-35.3490118655]  [-35.2276105202]\n",
       "32 -2.006340e+00     [-53.2764839081]    [-53.23432534]\n",
       "29 -0.000000e+00     [-56.0158079484]  [-55.9704649384]\n",
       "17 -0.000000e+00     [-131.785898247]  [-131.619497127]\n",
       "24 -1.566316e-12     [-146.541381122]  [-146.330323502]\n",
       "14  0.000000e+00     [-171.209462653]  [-169.044414673]\n",
       "8  -7.459446e+01     [-175.113111934]  [-175.169337023]\n",
       "5  -0.000000e+00     [-385.877226489]  [-385.406829153]\n",
       "7  -9.769098e+02      [-612.36986033]  [-612.497574562]\n",
       "4  -0.000000e+00     [-613.107647016]    [-613.2703877]\n",
       "38 -7.017275e+02      [-630.44488125]  [-630.612223856]\n",
       "3   0.000000e+00     [-813.835661992]   [-770.79129466]\n",
       "39 -6.797180e+02     [-795.506199788]  [-794.536450936]\n",
       "13 -5.073876e+00     [-821.563344135]  [-818.322389082]\n",
       "36 -1.303545e+03       [-987.8890991]  [-987.701879402]\n",
       "12 -0.000000e+00     [-1108.91666676]   [-1104.9090058]\n",
       "11 -8.037370e+01     [-1413.95779017]  [-1408.90733573]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.DataFrame({\"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)),\"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到缺省参数线性回归的系数与L2正则系数非常接近，lasso系数差异有些大。"
   ]
  }
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